GOES-R & JPSS: The Future of Weather Satellites

EWP

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Late Monday evening more thunderstorms developed over portions of southeastern Virginia. The environment was marginal; ~600 J/kg of MUCAPE and ~35 kts of effective bulk shear. By 0216 UTC 6 May 2014, the developing storm near Sussex county, VA had moderate MRMS MESH values (~0.85 inches). The moderate MESH and environmental fields yielded a 31% ProbSevere probability (Figure 1). Four minutes later, at 0220 UTC, the GOES scan from 0215 UTC captured both strong normalized vertical growth rate and strong glaciation rates associated with this storm, which caused the ProbSevere output to jump to 79%–demonstrating the value of satellite growth rates! The probabilities continued to increase as the radar storm intensity increased. The NWS issued a severe thunderstorm warning for this storm at 0241 UTC (21 minutes after the first ProbSevere value in excess of 50%) and first severe hail report was received at 0238 UTC (18 minutes after the first ProbSevere > 50%).

Additionally there were storms further east from the storm described above. These storms also exhibited ProbSevere values in excess of 50%. These storms had similar GOES inferred vertical growth rates and glaciation rates and similar MRMS MESH values, however these storms were not warned nor were associated with severe reports. This example demonstrates key points made in the NOAA/CIMSS ProbSevere training–the model skill is comparable, yet lower than an experienced forecaster, but often provides additional lead-time. One also wonders given the late evening timing of these additional storms if severe hail occurred but was never reported.

The NOAA/CIMSS ProbSevere Model is a statistical model that combines, NWP environmental conditions, satellite tracking and growth rates, and radar tracking and storm intensity to predict a developing storm will first produce severe weather in the near future (0-60 min). The NOAA/CIMSS ProbSevere model is being demonstrated to National Weather Service forecasters at 2014 Big Spring Experiment at the Hazardous Weather Testbed. NWS forecasters are encouraged to evaluate the ProbSevere model output as a means to increase confidence and lead-time in issuing initial severe warnings on developing storms. Today the weather is rather quiet across much of the country. A strong storm developed in southwestern Virgina and the evolution of the ProbSevere model performance on this storm is highlighted below. Of note, this storm achieves high probabilities and as of 2030 UTC, has been the only storm over CONUS today to achieve appreciable ProbSevere values.

Figure 1. The NOAA/CIMSS ProbSevere at 1950 UTC 5 May 2014–the first time the model produced probabilities above 50%. Radar data is not yet seen in this display as a domain switch was in-progress within HWT.

The NWS office in Blacksburg, VA issued a severe thunderstorm warning at 1952 UTC. The following two figures show the continued increase of NOAA/CIMSS ProbSevere probabilities.

Figure 2. NOAA/CIMSS ProbSevere at 1954 UTC 5 May 2014–probabilities are now 80%+.

This storm went on to produce severe hail (1.00″) at 2009 UTC–19 minutes after the initial ProbSevere > 50%. While this storm was not located in the a CWA of focus for this afternoon, forecasters are using this storm to gain confidence in the ProbSevere model and ask questions about some of the technical details of the model.

Today marks the first day of the 2014 Hazardous Weather Testbed (HWT) Spring Experiment, which will run through the first week in June (Experimental Warning Program (EWP) Big Spring Experiment will be off for Memorial Day week). The HWT provides the GOES-R Proving Ground with an opportunity to demonstrate baseline and future capabilities products associated with the next generation GOES-R geostationary satellite system that have the potential to improve short-range hazardous weather nowcasting and forecasting. The availability of GOES-R products will demonstrate, pre-launch, a portion of the full observing capability of the GOES-R system, subject to the constraints of existing data sources to emulate the satellite sensors.

GOES-R products being evaluated this year include: Synthetic Satellite Imagery, NearCast System, Convective Initiation, Probability of Severe Model, Overshooting Top Detection, PGLM Total Lightning products, Tracking Tool and Lightning Jump Algorithm. These products will be demonstrated by National Weather Service forecasters and broadcast meteorologists within a simulated warning operations environment using a real-time AWIPS-II framework within the HWT EWP. When and if appropriate and if time permits, several of these products may be demonstrated informally in the HWT Experimental Forecast Program (EFP) during the mid-afternoon when the EFP severe storm forecasts are being updated.

As in previous years, participants, in addition to myself and visiting scientists, will make frequent posts to the following blog regarding the days activities, product performance, product feedback, etc.: http://www.goesrhwt.blogspot.com/

– Bill Line, SPC/HWT Satellite Liaison

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